← Om Badgujar
Project / 03
Computer Vision · Group Project
AI Fitness Monitor
A real-time AI fitness monitoring app built with a team of four. MediaPipe extracts pose landmarks, a classifier scores squat form, and a Flutter client streams frames through a FastAPI backend for live feedback.
§ Problem
Why it exists.
Home workouts are effective only if the form is right — and there's no coach watching. We wanted a phone-first tool that could grade a squat in real time without shipping a wearable.
§ Approach
How it works.
- 01Owned the ML pipeline: pose landmark extraction with MediaPipe, feature engineering on joint angles, squat-posture classification with scikit-learn.
- 02Wrapped the model in a FastAPI service exposing a low-latency inference endpoint.
- 03Integrated the backend with a Flutter mobile app via REST for real-time posture prediction and feedback.
- 04Used Firebase for auth and session storage so users could track progress across workouts.
§ Key features
What it does.
- Real-time pose landmark extraction with MediaPipe on live camera frames.
- Squat-form classifier trained on engineered joint-angle features.
- FastAPI inference endpoint consumed by a Flutter mobile client.
- Firebase auth + session storage for tracking workouts over time.
§ Tech stack
Built with.
PythonFastAPIFlutterMediaPipeOpenCVScikit-learnFirebase
- ◆Real-time squat feedback on-device via the mobile app.
- ◆Clean separation of ML backend and Flutter client — either side can iterate independently.
- ◆Shipped as a four-person group project with clear module ownership.